Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations3679
Missing cells3727
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory901.9 KiB
Average record size in memory251.0 B

Variable types

Text5
Categorical15
Numeric8
Boolean27

Alerts

obtention_date has constant value "2019-03-29" Constant
bath_num is highly overall correlated with floor and 4 other fieldsHigh correlation
chimney is highly overall correlated with house_typeHigh correlation
condition_segunda mano/buen estado is highly overall correlated with condition_segunda mano/para reformarHigh correlation
condition_segunda mano/para reformar is highly overall correlated with condition_segunda mano/buen estadoHigh correlation
floor is highly overall correlated with bath_num and 1 other fieldsHigh correlation
garden is highly overall correlated with floorHigh correlation
house_type is highly overall correlated with chimneyHigh correlation
m2_real is highly overall correlated with bath_num and 3 other fieldsHigh correlation
m2_useful is highly overall correlated with bath_num and 2 other fieldsHigh correlation
price is highly overall correlated with bath_num and 1 other fieldsHigh correlation
room_num is highly overall correlated with bath_num and 2 other fieldsHigh correlation
air_conditioner is highly imbalanced (87.0%) Imbalance
chimney is highly imbalanced (85.0%) Imbalance
reduced_mobility is highly imbalanced (56.4%) Imbalance
swimming_pool is highly imbalanced (73.7%) Imbalance
condition_promoción de obra nueva is highly imbalanced (84.3%) Imbalance
condition_segunda mano/para reformar is highly imbalanced (51.5%) Imbalance
heating_calefacción central is highly imbalanced (81.6%) Imbalance
heating_calefacción central: gas is highly imbalanced (86.1%) Imbalance
heating_calefacción central: gasoil is highly imbalanced (90.6%) Imbalance
heating_calefacción individual is highly imbalanced (64.5%) Imbalance
heating_calefacción individual: bomba de frío/calor is highly imbalanced (99.0%) Imbalance
heating_calefacción individual: eléctrica is highly imbalanced (95.1%) Imbalance
heating_calefacción individual: gas propano/butano is highly imbalanced (93.2%) Imbalance
heating_no dispone de calefacción is highly imbalanced (96.2%) Imbalance
orientation_este is highly imbalanced (74.3%) Imbalance
orientation_este, oeste is highly imbalanced (55.7%) Imbalance
orientation_norte is highly imbalanced (84.6%) Imbalance
orientation_norte, este is highly imbalanced (87.5%) Imbalance
orientation_norte, este, oeste is highly imbalanced (93.0%) Imbalance
orientation_norte, oeste is highly imbalanced (91.7%) Imbalance
orientation_norte, sur is highly imbalanced (61.8%) Imbalance
orientation_norte, sur, este is highly imbalanced (93.7%) Imbalance
orientation_norte, sur, este, oeste is highly imbalanced (78.9%) Imbalance
orientation_norte, sur, oeste is highly imbalanced (93.5%) Imbalance
orientation_oeste is highly imbalanced (78.2%) Imbalance
orientation_sur is highly imbalanced (56.1%) Imbalance
orientation_sur, este is highly imbalanced (76.7%) Imbalance
orientation_sur, este, oeste is highly imbalanced (92.8%) Imbalance
orientation_sur, oeste is highly imbalanced (76.7%) Imbalance
energetic_certif has 1300 (35.3%) missing values Missing
floor has 499 (13.6%) missing values Missing
loc_district has 309 (8.4%) missing values Missing
loc_neigh has 1619 (44.0%) missing values Missing
m2_real is highly skewed (γ1 = 38.76028739) Skewed
house_id has unique values Unique
house_type has 428 (11.6%) zeros Zeros
room_num has 37 (1.0%) zeros Zeros

Reproduction

Analysis started2025-08-03 13:08:20.780441
Analysis finished2025-08-03 13:08:43.442674
Duration22.66 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct249
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:43.655316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length34
Mean length34.423485
Min length29

Characters and Unicode

Total characters126644
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)1.4%

Sample

1st rowAnuncio actualizado el 27 de marzo
2nd rowmás de 5 meses sin actualizar
3rd rowmás de 5 meses sin actualizar
4th rowmás de 5 meses sin actualizar
5th rowmás de 5 meses sin actualizar
ValueCountFrequency (%)
de 3679
16.7%
actualizado 3581
16.2%
anuncio 3581
16.2%
el 3581
16.2%
marzo 1866
8.5%
febrero 699
 
3.2%
enero 454
 
2.1%
1 276
 
1.3%
24 259
 
1.2%
27 228
 
1.0%
Other values (41) 3870
17.5%
2025-08-03T13:08:44.714892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18395
14.5%
a 12977
10.2%
e 10587
 
8.4%
o 10556
 
8.3%
i 7901
 
6.2%
n 7899
 
6.2%
c 7454
 
5.9%
u 7410
 
5.9%
d 7372
 
5.8%
l 7327
 
5.8%
Other values (24) 28766
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 126644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18395
14.5%
a 12977
10.2%
e 10587
 
8.4%
o 10556
 
8.3%
i 7901
 
6.2%
n 7899
 
6.2%
c 7454
 
5.9%
u 7410
 
5.9%
d 7372
 
5.8%
l 7327
 
5.8%
Other values (24) 28766
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 126644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18395
14.5%
a 12977
10.2%
e 10587
 
8.4%
o 10556
 
8.3%
i 7901
 
6.2%
n 7899
 
6.2%
c 7454
 
5.9%
u 7410
 
5.9%
d 7372
 
5.8%
l 7327
 
5.8%
Other values (24) 28766
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 126644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18395
14.5%
a 12977
10.2%
e 10587
 
8.4%
o 10556
 
8.3%
i 7901
 
6.2%
n 7899
 
6.2%
c 7454
 
5.9%
u 7410
 
5.9%
d 7372
 
5.8%
l 7327
 
5.8%
Other values (24) 28766
22.7%

air_conditioner
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
3613 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

Length

2025-08-03T13:08:44.835805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:44.909886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3613
98.2%
1 66
 
1.8%

balcony
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
3240 
1
439 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

Length

2025-08-03T13:08:44.995562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:45.075716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3240
88.1%
1 439
 
11.9%

bath_num
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9005164
Minimum0
Maximum34
Zeros27
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:45.164610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum34
Range34
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1187741
Coefficient of variation (CV)0.58866847
Kurtosis196.53554
Mean1.9005164
Median Absolute Deviation (MAD)1
Skewness8.2029995
Sum6992
Variance1.2516555
MonotonicityNot monotonic
2025-08-03T13:08:45.274935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 1602
43.5%
1 1347
36.6%
3 500
 
13.6%
4 154
 
4.2%
5 32
 
0.9%
0 27
 
0.7%
6 8
 
0.2%
15 2
 
0.1%
8 2
 
0.1%
7 1
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 27
 
0.7%
1 1347
36.6%
2 1602
43.5%
3 500
 
13.6%
4 154
 
4.2%
5 32
 
0.9%
6 8
 
0.2%
7 1
 
< 0.1%
8 2
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
34 1
 
< 0.1%
15 2
 
0.1%
11 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 2
 
0.1%
7 1
 
< 0.1%
6 8
 
0.2%
5 32
 
0.9%
4 154
4.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
2528 
1
1151 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

Length

2025-08-03T13:08:45.395801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:45.468351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

Most occurring characters

ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2528
68.7%
1 1151
31.3%

chimney
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
3600 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

Length

2025-08-03T13:08:45.572175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:45.646492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3600
97.9%
1 79
 
2.1%

construct_date
Real number (ℝ)

Distinct104
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1990.1017
Minimum1700
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:45.768244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile1966.9
Q11993
median1993
Q31993
95-th percentile2008
Maximum2021
Range321
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.279343
Coefficient of variation (CV)0.010190104
Kurtosis63.297899
Mean1990.1017
Median Absolute Deviation (MAD)0
Skewness-6.6013092
Sum7321584
Variance411.25176
MonotonicityNot monotonic
2025-08-03T13:08:45.953694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1993 2578
70.1%
2008 69
 
1.9%
2006 53
 
1.4%
2009 43
 
1.2%
1980 38
 
1.0%
2007 37
 
1.0%
2010 35
 
1.0%
1975 34
 
0.9%
2004 31
 
0.8%
2005 31
 
0.8%
Other values (94) 730
 
19.8%
ValueCountFrequency (%)
1700 3
 
0.1%
1780 1
 
< 0.1%
1800 8
0.2%
1829 1
 
< 0.1%
1830 1
 
< 0.1%
1840 1
 
< 0.1%
1850 5
0.1%
1880 2
 
0.1%
1883 1
 
< 0.1%
1884 1
 
< 0.1%
ValueCountFrequency (%)
2021 1
 
< 0.1%
2020 2
 
0.1%
2019 7
 
0.2%
2018 12
0.3%
2017 2
 
0.1%
2016 1
 
< 0.1%
2015 1
 
< 0.1%
2014 4
 
0.1%
2013 5
 
0.1%
2012 25
0.7%

energetic_certif
Categorical

Missing 

Distinct3
Distinct (%)0.1%
Missing1300
Missing (%)35.3%
Memory size28.9 KiB
en trámite
1609 
no indicado
728 
inmueble exento
 
42

Length

Max length15
Median length10
Mean length10.394283
Min length10

Characters and Unicode

Total characters24728
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno indicado
2nd rowno indicado
3rd rowen trámite
4th rowno indicado
5th rowno indicado

Common Values

ValueCountFrequency (%)
en trámite 1609
43.7%
no indicado 728
19.8%
inmueble exento 42
 
1.1%
(Missing) 1300
35.3%

Length

2025-08-03T13:08:46.095149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:46.175822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
en 1609
33.8%
trámite 1609
33.8%
no 728
15.3%
indicado 728
15.3%
inmueble 42
 
0.9%
exento 42
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 3386
13.7%
t 3260
13.2%
n 3149
12.7%
i 3107
12.6%
2379
9.6%
m 1651
6.7%
á 1609
6.5%
r 1609
6.5%
o 1498
6.1%
d 1456
5.9%
Other values (6) 1624
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3386
13.7%
t 3260
13.2%
n 3149
12.7%
i 3107
12.6%
2379
9.6%
m 1651
6.7%
á 1609
6.5%
r 1609
6.5%
o 1498
6.1%
d 1456
5.9%
Other values (6) 1624
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3386
13.7%
t 3260
13.2%
n 3149
12.7%
i 3107
12.6%
2379
9.6%
m 1651
6.7%
á 1609
6.5%
r 1609
6.5%
o 1498
6.1%
d 1456
5.9%
Other values (6) 1624
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3386
13.7%
t 3260
13.2%
n 3149
12.7%
i 3107
12.6%
2379
9.6%
m 1651
6.7%
á 1609
6.5%
r 1609
6.5%
o 1498
6.1%
d 1456
5.9%
Other values (6) 1624
6.6%

floor
Categorical

High correlation  Missing 

Distinct45
Distinct (%)1.4%
Missing499
Missing (%)13.6%
Memory size28.9 KiB
planta 1ª exterior
523 
planta 2ª exterior
336 
3 plantas
325 
2 plantas
290 
planta 3ª exterior
288 
Other values (40)
1418 

Length

Max length20
Median length18
Mean length14.065409
Min length4

Characters and Unicode

Total characters44728
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.3%

Sample

1st row2 plantas
2nd rowplanta 2ª exterior
3rd row3 plantas
4th row3 plantas
5th rowplanta 1ª exterior

Common Values

ValueCountFrequency (%)
planta 1ª exterior 523
14.2%
planta 2ª exterior 336
9.1%
3 plantas 325
8.8%
2 plantas 290
7.9%
planta 3ª exterior 288
7.8%
planta 4ª exterior 210
 
5.7%
exterior 205
 
5.6%
planta 5ª exterior 148
 
4.0%
planta 1ª 108
 
2.9%
bajo exterior 87
 
2.4%
Other values (35) 660
17.9%
(Missing) 499
13.6%

Length

2025-08-03T13:08:46.303067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
planta 2176
27.5%
exterior 2008
25.4%
plantas 683
 
8.6%
639
 
8.1%
413
 
5.2%
354
 
4.5%
3 325
 
4.1%
2 290
 
3.7%
270
 
3.4%
204
 
2.6%
Other values (16) 544
 
6.9%

Most occurring characters

ValueCountFrequency (%)
a 5850
13.1%
t 4964
11.1%
4726
10.6%
r 4160
9.3%
e 4113
9.2%
n 2956
 
6.6%
p 2875
 
6.4%
l 2875
 
6.4%
o 2172
 
4.9%
i 2137
 
4.8%
Other values (18) 7900
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44728
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 5850
13.1%
t 4964
11.1%
4726
10.6%
r 4160
9.3%
e 4113
9.2%
n 2956
 
6.6%
p 2875
 
6.4%
l 2875
 
6.4%
o 2172
 
4.9%
i 2137
 
4.8%
Other values (18) 7900
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44728
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 5850
13.1%
t 4964
11.1%
4726
10.6%
r 4160
9.3%
e 4113
9.2%
n 2956
 
6.6%
p 2875
 
6.4%
l 2875
 
6.4%
o 2172
 
4.9%
i 2137
 
4.8%
Other values (18) 7900
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44728
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 5850
13.1%
t 4964
11.1%
4726
10.6%
r 4160
9.3%
e 4113
9.2%
n 2956
 
6.6%
p 2875
 
6.4%
l 2875
 
6.4%
o 2172
 
4.9%
i 2137
 
4.8%
Other values (18) 7900
17.7%

garage
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
2035 
1
1644 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

Length

2025-08-03T13:08:46.430171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:46.510286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

Most occurring characters

ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2035
55.3%
1 1644
44.7%

garden
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
2724 
1
955 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

Length

2025-08-03T13:08:46.617659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:46.701696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

Most occurring characters

ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2724
74.0%
1 955
 
26.0%

house_id
Real number (ℝ)

Unique 

Distinct3679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60997534
Minimum299398
Maximum84861368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:46.840507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum299398
5-th percentile26186451
Q136519690
median81734822
Q384065158
95-th percentile84695083
Maximum84861368
Range84561970
Interquartile range (IQR)47545468

Descriptive statistics

Standard deviation26341717
Coefficient of variation (CV)0.43184888
Kurtosis-1.2861768
Mean60997534
Median Absolute Deviation (MAD)2932772
Skewness-0.49126872
Sum2.2440993 × 1011
Variance6.9388605 × 1014
MonotonicityNot monotonic
2025-08-03T13:08:46.987161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40567154 1
 
< 0.1%
81717634 1
 
< 0.1%
29588074 1
 
< 0.1%
37453116 1
 
< 0.1%
82568918 1
 
< 0.1%
29135242 1
 
< 0.1%
36141335 1
 
< 0.1%
38440698 1
 
< 0.1%
81178589 1
 
< 0.1%
366036 1
 
< 0.1%
Other values (3669) 3669
99.7%
ValueCountFrequency (%)
299398 1
< 0.1%
301679 1
< 0.1%
305251 1
< 0.1%
306081 1
< 0.1%
310610 1
< 0.1%
310733 1
< 0.1%
312743 1
< 0.1%
314071 1
< 0.1%
316443 1
< 0.1%
316943 1
< 0.1%
ValueCountFrequency (%)
84861368 1
< 0.1%
84857219 1
< 0.1%
84855193 1
< 0.1%
84854931 1
< 0.1%
84854882 1
< 0.1%
84854719 1
< 0.1%
84854690 1
< 0.1%
84854635 1
< 0.1%
84854593 1
< 0.1%
84854563 1
< 0.1%

house_type
Real number (ℝ)

High correlation  Zeros 

Distinct15
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2063061
Minimum0
Maximum14
Zeros428
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:47.105353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median3
Q33
95-th percentile8
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0096568
Coefficient of variation (CV)0.62678258
Kurtosis2.9372651
Mean3.2063061
Median Absolute Deviation (MAD)0
Skewness1.2260683
Sum11796
Variance4.0387204
MonotonicityNot monotonic
2025-08-03T13:08:47.228715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 2395
65.1%
0 428
 
11.6%
5 226
 
6.1%
2 136
 
3.7%
6 125
 
3.4%
9 121
 
3.3%
8 100
 
2.7%
1 77
 
2.1%
4 33
 
0.9%
7 15
 
0.4%
Other values (5) 23
 
0.6%
ValueCountFrequency (%)
0 428
 
11.6%
1 77
 
2.1%
2 136
 
3.7%
3 2395
65.1%
4 33
 
0.9%
5 226
 
6.1%
6 125
 
3.4%
7 15
 
0.4%
8 100
 
2.7%
9 121
 
3.3%
ValueCountFrequency (%)
14 2
 
0.1%
13 2
 
0.1%
12 2
 
0.1%
11 8
 
0.2%
10 9
 
0.2%
9 121
3.3%
8 100
2.7%
7 15
 
0.4%
6 125
3.4%
5 226
6.1%

lift
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
1.0
3100 
0.0
579 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11037
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3100
84.3%
0.0 579
 
15.7%

Length

2025-08-03T13:08:47.356978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:47.442688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3100
84.3%
0.0 579
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 4258
38.6%
. 3679
33.3%
1 3100
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4258
38.6%
. 3679
33.3%
1 3100
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4258
38.6%
. 3679
33.3%
1 3100
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4258
38.6%
. 3679
33.3%
1 3100
28.1%
Distinct51
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:47.661931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length15
Mean length12.600163
Min length4

Characters and Unicode

Total characters46356
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUrcabustaiz
2nd rowLegutiano
3rd rowAramaio
4th rowUrcabustaiz
5th rowUrcabustaiz
ValueCountFrequency (%)
vitoria-gasteiz 2197
54.9%
llodio 147
 
3.7%
de 122
 
3.0%
labastida 101
 
2.5%
alegria-dulantzi 85
 
2.1%
oión 73
 
1.8%
iruña 69
 
1.7%
oca 69
 
1.7%
zigoitia 68
 
1.7%
ribera 65
 
1.6%
Other values (50) 1006
25.1%
2025-08-03T13:08:48.057905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 7895
17.0%
a 6704
14.5%
t 4936
10.6%
r 3154
 
6.8%
o 2936
 
6.3%
e 2935
 
6.3%
z 2448
 
5.3%
s 2397
 
5.2%
- 2321
 
5.0%
V 2255
 
4.9%
Other values (36) 8375
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7895
17.0%
a 6704
14.5%
t 4936
10.6%
r 3154
 
6.8%
o 2936
 
6.3%
e 2935
 
6.3%
z 2448
 
5.3%
s 2397
 
5.2%
- 2321
 
5.0%
V 2255
 
4.9%
Other values (36) 8375
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7895
17.0%
a 6704
14.5%
t 4936
10.6%
r 3154
 
6.8%
o 2936
 
6.3%
e 2935
 
6.3%
z 2448
 
5.3%
s 2397
 
5.2%
- 2321
 
5.0%
V 2255
 
4.9%
Other values (36) 8375
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7895
17.0%
a 6704
14.5%
t 4936
10.6%
r 3154
 
6.8%
o 2936
 
6.3%
e 2935
 
6.3%
z 2448
 
5.3%
s 2397
 
5.2%
- 2321
 
5.0%
V 2255
 
4.9%
Other values (36) 8375
18.1%

loc_district
Text

Missing 

Distinct1103
Distinct (%)32.7%
Missing309
Missing (%)8.4%
Memory size28.9 KiB
2025-08-03T13:08:48.382573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length37
Mean length21.156973
Min length4

Characters and Unicode

Total characters71299
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1007 ?
Unique (%)29.9%

Sample

1st rowLa iglesia
2nd rowla Union Plazatxoa, 8
3rd rowCalle Etxaguen s/n
4th rowCP 01449, 1449 CP
5th rowCalle Nueva Plaza, 9
ValueCountFrequency (%)
distrito 2197
20.5%
987
 
9.2%
calle 583
 
5.4%
lakua 307
 
2.9%
el 272
 
2.5%
casco 206
 
1.9%
viejo 206
 
1.9%
ariznabarra 202
 
1.9%
zabalgana 202
 
1.9%
centro 201
 
1.9%
Other values (824) 5361
50.0%
2025-08-03T13:08:48.891442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8539
12.0%
7354
 
10.3%
i 6874
 
9.6%
r 6412
 
9.0%
t 6008
 
8.4%
o 4442
 
6.2%
l 3157
 
4.4%
s 3031
 
4.3%
e 2843
 
4.0%
n 2575
 
3.6%
Other values (67) 20064
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8539
12.0%
7354
 
10.3%
i 6874
 
9.6%
r 6412
 
9.0%
t 6008
 
8.4%
o 4442
 
6.2%
l 3157
 
4.4%
s 3031
 
4.3%
e 2843
 
4.0%
n 2575
 
3.6%
Other values (67) 20064
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8539
12.0%
7354
 
10.3%
i 6874
 
9.6%
r 6412
 
9.0%
t 6008
 
8.4%
o 4442
 
6.2%
l 3157
 
4.4%
s 3031
 
4.3%
e 2843
 
4.0%
n 2575
 
3.6%
Other values (67) 20064
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8539
12.0%
7354
 
10.3%
i 6874
 
9.6%
r 6412
 
9.0%
t 6008
 
8.4%
o 4442
 
6.2%
l 3157
 
4.4%
s 3031
 
4.3%
e 2843
 
4.0%
n 2575
 
3.6%
Other values (67) 20064
28.1%
Distinct2429
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:49.235944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length155
Median length100
Mean length63.896167
Min length16

Characters and Unicode

Total characters235074
Distinct characters81
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2027 ?
Unique (%)55.1%

Sample

1st row La iglesia , Urcabustaiz , Zuya, Álava
2nd row la Union Plazatxoa, 8 , Legutiano , Zuya, Álava
3rd row Calle Etxaguen s/n , Aramaio , Zuya, Álava
4th row CP 01449, 1449 CP , Urcabustaiz , Zuya, Álava
5th row Calle Nueva Plaza, 9 , Urcabustaiz , Zuya, Álava
ValueCountFrequency (%)
10686
29.2%
álava 3692
 
10.1%
vitoria-gasteiz 2451
 
6.7%
distrito 2197
 
6.0%
calle 1710
 
4.7%
de 650
 
1.8%
alavesa 424
 
1.2%
laguardia-rioja 334
 
0.9%
ayala 327
 
0.9%
lakua 308
 
0.8%
Other values (1417) 13879
37.9%
2025-08-03T13:08:49.823113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40337
17.2%
a 32945
14.0%
i 18464
 
7.9%
t 12793
 
5.4%
r 12733
 
5.4%
, 12361
 
5.3%
l 12217
 
5.2%
e 10632
 
4.5%
o 9934
 
4.2%
s 7148
 
3.0%
Other values (71) 65510
27.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 235074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
40337
17.2%
a 32945
14.0%
i 18464
 
7.9%
t 12793
 
5.4%
r 12733
 
5.4%
, 12361
 
5.3%
l 12217
 
5.2%
e 10632
 
4.5%
o 9934
 
4.2%
s 7148
 
3.0%
Other values (71) 65510
27.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 235074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
40337
17.2%
a 32945
14.0%
i 18464
 
7.9%
t 12793
 
5.4%
r 12733
 
5.4%
, 12361
 
5.3%
l 12217
 
5.2%
e 10632
 
4.5%
o 9934
 
4.2%
s 7148
 
3.0%
Other values (71) 65510
27.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 235074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
40337
17.2%
a 32945
14.0%
i 18464
 
7.9%
t 12793
 
5.4%
r 12733
 
5.4%
, 12361
 
5.3%
l 12217
 
5.2%
e 10632
 
4.5%
o 9934
 
4.2%
s 7148
 
3.0%
Other values (71) 65510
27.9%

loc_neigh
Text

Missing 

Distinct1299
Distinct (%)63.1%
Missing1619
Missing (%)44.0%
Memory size28.9 KiB
2025-08-03T13:08:50.201701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length62
Median length47
Mean length21.431068
Min length3

Characters and Unicode

Total characters44148
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)48.5%

Sample

1st rowCarretera altube, 622
2nd rowCalle Municipal Plaza, 3
3rd rowPlaza Municipal, 7
4th rowPlaza Nueva, 3 NmeroD
5th rowPlaza Municipal, 9
ValueCountFrequency (%)
calle 1107
 
16.9%
de 426
 
6.5%
vitoria-gasteiz 238
 
3.6%
kalea 186
 
2.8%
avenida 94
 
1.4%
los 85
 
1.3%
s/n 76
 
1.2%
hiribidea 61
 
0.9%
urb 58
 
0.9%
la 55
 
0.8%
Other values (809) 4145
63.5%
2025-08-03T13:08:50.792559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6011
13.6%
4471
 
10.1%
e 4047
 
9.2%
l 3743
 
8.5%
i 2763
 
6.3%
r 2403
 
5.4%
o 1991
 
4.5%
n 1765
 
4.0%
t 1522
 
3.4%
C 1383
 
3.1%
Other values (69) 14049
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6011
13.6%
4471
 
10.1%
e 4047
 
9.2%
l 3743
 
8.5%
i 2763
 
6.3%
r 2403
 
5.4%
o 1991
 
4.5%
n 1765
 
4.0%
t 1522
 
3.4%
C 1383
 
3.1%
Other values (69) 14049
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6011
13.6%
4471
 
10.1%
e 4047
 
9.2%
l 3743
 
8.5%
i 2763
 
6.3%
r 2403
 
5.4%
o 1991
 
4.5%
n 1765
 
4.0%
t 1522
 
3.4%
C 1383
 
3.1%
Other values (69) 14049
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6011
13.6%
4471
 
10.1%
e 4047
 
9.2%
l 3743
 
8.5%
i 2763
 
6.3%
r 2403
 
5.4%
o 1991
 
4.5%
n 1765
 
4.0%
t 1522
 
3.4%
C 1383
 
3.1%
Other values (69) 14049
31.8%

loc_zone
Categorical

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
Álava
2374 
Laguardia-Rioja Alavesa, Álava
319 
Añana, Álava
266 
Ayala, Álava
254 
Zuya, Álava
 
198
Other values (2)
268 

Length

Max length31
Median length5
Mean length9.7415058
Min length5

Characters and Unicode

Total characters35839
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZuya, Álava
2nd rowZuya, Álava
3rd rowZuya, Álava
4th rowZuya, Álava
5th rowZuya, Álava

Common Values

ValueCountFrequency (%)
Álava 2374
64.5%
Laguardia-Rioja Alavesa, Álava 319
 
8.7%
Añana, Álava 266
 
7.2%
Ayala, Álava 254
 
6.9%
Zuya, Álava 198
 
5.4%
Salvatierra, Álava 179
 
4.9%
Campezo- Montaña Alavesa, Álava 89
 
2.4%

Length

2025-08-03T13:08:50.962388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:51.082313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
álava 3679
67.1%
alavesa 408
 
7.4%
laguardia-rioja 319
 
5.8%
añana 266
 
4.9%
ayala 254
 
4.6%
zuya 198
 
3.6%
salvatierra 179
 
3.3%
campezo 89
 
1.6%
montaña 89
 
1.6%

Most occurring characters

ValueCountFrequency (%)
a 11492
32.1%
l 4520
 
12.6%
v 4266
 
11.9%
Á 3679
 
10.3%
1802
 
5.0%
, 1305
 
3.6%
A 928
 
2.6%
i 817
 
2.3%
r 677
 
1.9%
e 676
 
1.9%
Other values (20) 5677
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11492
32.1%
l 4520
 
12.6%
v 4266
 
11.9%
Á 3679
 
10.3%
1802
 
5.0%
, 1305
 
3.6%
A 928
 
2.6%
i 817
 
2.3%
r 677
 
1.9%
e 676
 
1.9%
Other values (20) 5677
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11492
32.1%
l 4520
 
12.6%
v 4266
 
11.9%
Á 3679
 
10.3%
1802
 
5.0%
, 1305
 
3.6%
A 928
 
2.6%
i 817
 
2.3%
r 677
 
1.9%
e 676
 
1.9%
Other values (20) 5677
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11492
32.1%
l 4520
 
12.6%
v 4266
 
11.9%
Á 3679
 
10.3%
1802
 
5.0%
, 1305
 
3.6%
A 928
 
2.6%
i 817
 
2.3%
r 677
 
1.9%
e 676
 
1.9%
Other values (20) 5677
15.8%

m2_real
Real number (ℝ)

High correlation  Skewed 

Distinct484
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean456.45828
Minimum2
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:51.245307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile60
Q180
median100
Q3175
95-th percentile1301.6
Maximum200000
Range199998
Interquartile range (IQR)95

Descriptive statistics

Standard deviation3931.0593
Coefficient of variation (CV)8.6120889
Kurtosis1848.7248
Mean456.45828
Median Absolute Deviation (MAD)29
Skewness38.760287
Sum1679310
Variance15453227
MonotonicityNot monotonic
2025-08-03T13:08:51.419571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 176
 
4.8%
70 127
 
3.5%
80 125
 
3.4%
100 108
 
2.9%
75 96
 
2.6%
85 93
 
2.5%
65 87
 
2.4%
95 74
 
2.0%
60 67
 
1.8%
120 64
 
1.7%
Other values (474) 2662
72.4%
ValueCountFrequency (%)
2 1
< 0.1%
4 2
0.1%
12 1
< 0.1%
15 2
0.1%
18 1
< 0.1%
20 2
0.1%
25 2
0.1%
30 2
0.1%
35 1
< 0.1%
36 1
< 0.1%
ValueCountFrequency (%)
200000 1
< 0.1%
70400 1
< 0.1%
51000 1
< 0.1%
42000 1
< 0.1%
40000 1
< 0.1%
30000 2
0.1%
25376 1
< 0.1%
18252 1
< 0.1%
17500 1
< 0.1%
17000 1
< 0.1%

m2_useful
Real number (ℝ)

High correlation 

Distinct272
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.07529
Minimum30
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:51.583526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile59
Q180
median87
Q394
95-th percentile250
Maximum2000
Range1970
Interquartile range (IQR)14

Descriptive statistics

Standard deviation80.774467
Coefficient of variation (CV)0.74739068
Kurtosis103.14971
Mean108.07529
Median Absolute Deviation (MAD)7
Skewness7.0341573
Sum397609
Variance6524.5144
MonotonicityNot monotonic
2025-08-03T13:08:51.768176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87 1388
37.7%
90 123
 
3.3%
70 92
 
2.5%
80 89
 
2.4%
75 76
 
2.1%
60 62
 
1.7%
65 55
 
1.5%
85 51
 
1.4%
78 41
 
1.1%
68 41
 
1.1%
Other values (262) 1661
45.1%
ValueCountFrequency (%)
30 2
 
0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
39 1
 
< 0.1%
41 1
 
< 0.1%
42 1
 
< 0.1%
43 5
0.1%
ValueCountFrequency (%)
2000 1
< 0.1%
1000 2
0.1%
876 1
< 0.1%
870 1
< 0.1%
831 1
< 0.1%
784 1
< 0.1%
680 1
< 0.1%
610 1
< 0.1%
603 1
< 0.1%
600 2
0.1%

obtention_date
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2019-03-29
3679 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters36790
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-03-29
2nd row2019-03-29
3rd row2019-03-29
4th row2019-03-29
5th row2019-03-29

Common Values

ValueCountFrequency (%)
2019-03-29 3679
100.0%

Length

2025-08-03T13:08:51.940665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:52.024618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2019-03-29 3679
100.0%

Most occurring characters

ValueCountFrequency (%)
2 7358
20.0%
0 7358
20.0%
9 7358
20.0%
- 7358
20.0%
1 3679
10.0%
3 3679
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36790
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 7358
20.0%
0 7358
20.0%
9 7358
20.0%
- 7358
20.0%
1 3679
10.0%
3 3679
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36790
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 7358
20.0%
0 7358
20.0%
9 7358
20.0%
- 7358
20.0%
1 3679
10.0%
3 3679
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36790
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 7358
20.0%
0 7358
20.0%
9 7358
20.0%
- 7358
20.0%
1 3679
10.0%
3 3679
10.0%

price
Real number (ℝ)

High correlation 

Distinct677
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244807.69
Minimum9300
Maximum1700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:52.160712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9300
5-th percentile75000
Q1145000
median199000
Q3298000
95-th percentile570000
Maximum1700000
Range1690700
Interquartile range (IQR)153000

Descriptive statistics

Standard deviation163366.11
Coefficient of variation (CV)0.66732427
Kurtosis11.031586
Mean244807.69
Median Absolute Deviation (MAD)71000
Skewness2.412962
Sum9.0064748 × 108
Variance2.6688486 × 1010
MonotonicityNot monotonic
2025-08-03T13:08:52.352373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150000 80
 
2.2%
120000 71
 
1.9%
180000 70
 
1.9%
175000 68
 
1.8%
200000 61
 
1.7%
160000 61
 
1.7%
130000 52
 
1.4%
210000 52
 
1.4%
110000 50
 
1.4%
165000 44
 
1.2%
Other values (667) 3070
83.4%
ValueCountFrequency (%)
9300 1
 
< 0.1%
15000 2
0.1%
16000 1
 
< 0.1%
18000 1
 
< 0.1%
21500 1
 
< 0.1%
23000 1
 
< 0.1%
25000 1
 
< 0.1%
26500 3
0.1%
27000 2
0.1%
30000 4
0.1%
ValueCountFrequency (%)
1700000 4
0.1%
1500000 1
 
< 0.1%
1350000 1
 
< 0.1%
1260000 1
 
< 0.1%
1135000 1
 
< 0.1%
1100000 2
 
0.1%
995000 1
 
< 0.1%
980000 2
 
0.1%
975000 1
 
< 0.1%
950000 6
0.2%

reduced_mobility
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
3348 
1
 
331

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

Length

2025-08-03T13:08:52.536336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:52.633031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3348
91.0%
1 331
 
9.0%

room_num
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1726013
Minimum0
Maximum30
Zeros37
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-08-03T13:08:52.717589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum30
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3723953
Coefficient of variation (CV)0.4325773
Kurtosis50.647777
Mean3.1726013
Median Absolute Deviation (MAD)1
Skewness3.9445597
Sum11672
Variance1.8834688
MonotonicityNot monotonic
2025-08-03T13:08:52.859590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 1734
47.1%
2 786
21.4%
4 644
 
17.5%
5 232
 
6.3%
1 105
 
2.9%
6 65
 
1.8%
0 37
 
1.0%
7 34
 
0.9%
8 17
 
0.5%
10 10
 
0.3%
Other values (6) 15
 
0.4%
ValueCountFrequency (%)
0 37
 
1.0%
1 105
 
2.9%
2 786
21.4%
3 1734
47.1%
4 644
 
17.5%
5 232
 
6.3%
6 65
 
1.8%
7 34
 
0.9%
8 17
 
0.5%
9 6
 
0.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
18 1
 
< 0.1%
15 2
 
0.1%
13 1
 
< 0.1%
12 4
 
0.1%
10 10
 
0.3%
9 6
 
0.2%
8 17
 
0.5%
7 34
0.9%
6 65
1.8%

storage_room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
1
2392 
0
1287 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

Length

2025-08-03T13:08:52.987537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:53.093432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

Most occurring characters

ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2392
65.0%
0 1287
35.0%

swimming_pool
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
0
3515 
1
 
164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

Length

2025-08-03T13:08:53.202134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:53.341461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3515
95.5%
1 164
 
4.5%

terrace
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
1
1970 
0
1709 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3679
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%

Length

2025-08-03T13:08:53.513761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-03T13:08:53.637281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%

Most occurring characters

ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1970
53.5%
0 1709
46.5%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3595 
True
 
84
ValueCountFrequency (%)
False 3595
97.7%
True 84
 
2.3%
2025-08-03T13:08:53.717689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

condition_segunda mano/buen estado
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
True
3021 
False
658 
ValueCountFrequency (%)
True 3021
82.1%
False 658
 
17.9%
2025-08-03T13:08:53.797510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

condition_segunda mano/para reformar
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3292 
True
387 
ValueCountFrequency (%)
False 3292
89.5%
True 387
 
10.5%
2025-08-03T13:08:53.893418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

heating_calefacción central
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3576 
True
 
103
ValueCountFrequency (%)
False 3576
97.2%
True 103
 
2.8%
2025-08-03T13:08:53.988502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3607 
True
 
72
ValueCountFrequency (%)
False 3607
98.0%
True 72
 
2.0%
2025-08-03T13:08:54.078530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3635 
True
 
44
ValueCountFrequency (%)
False 3635
98.8%
True 44
 
1.2%
2025-08-03T13:08:54.179743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3432 
True
 
247
ValueCountFrequency (%)
False 3432
93.3%
True 247
 
6.7%
2025-08-03T13:08:54.262576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3676 
True
 
3
ValueCountFrequency (%)
False 3676
99.9%
True 3
 
0.1%
2025-08-03T13:08:54.338760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3659 
True
 
20
ValueCountFrequency (%)
False 3659
99.5%
True 20
 
0.5%
2025-08-03T13:08:54.417929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3175 
True
504 
ValueCountFrequency (%)
False 3175
86.3%
True 504
 
13.7%
2025-08-03T13:08:54.504766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3649 
True
 
30
ValueCountFrequency (%)
False 3649
99.2%
True 30
 
0.8%
2025-08-03T13:08:54.577861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3664 
True
 
15
ValueCountFrequency (%)
False 3664
99.6%
True 15
 
0.4%
2025-08-03T13:08:54.676128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_este
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3520 
True
 
159
ValueCountFrequency (%)
False 3520
95.7%
True 159
 
4.3%
2025-08-03T13:08:54.803963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_este, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3341 
True
338 
ValueCountFrequency (%)
False 3341
90.8%
True 338
 
9.2%
2025-08-03T13:08:54.899713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3597 
True
 
82
ValueCountFrequency (%)
False 3597
97.8%
True 82
 
2.2%
2025-08-03T13:08:54.977921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, este
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3616 
True
 
63
ValueCountFrequency (%)
False 3616
98.3%
True 63
 
1.7%
2025-08-03T13:08:55.045700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3648 
True
 
31
ValueCountFrequency (%)
False 3648
99.2%
True 31
 
0.8%
2025-08-03T13:08:55.124491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3641 
True
 
38
ValueCountFrequency (%)
False 3641
99.0%
True 38
 
1.0%
2025-08-03T13:08:55.207336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, sur
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3405 
True
 
274
ValueCountFrequency (%)
False 3405
92.6%
True 274
 
7.4%
2025-08-03T13:08:55.299219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_norte, sur, este
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3652 
True
 
27
ValueCountFrequency (%)
False 3652
99.3%
True 27
 
0.7%
2025-08-03T13:08:55.382543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3556 
True
 
123
ValueCountFrequency (%)
False 3556
96.7%
True 123
 
3.3%
2025-08-03T13:08:55.481692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3651 
True
 
28
ValueCountFrequency (%)
False 3651
99.2%
True 28
 
0.8%
2025-08-03T13:08:55.562498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_oeste
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3551 
True
 
128
ValueCountFrequency (%)
False 3551
96.5%
True 128
 
3.5%
2025-08-03T13:08:55.643969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3345 
True
 
334
ValueCountFrequency (%)
False 3345
90.9%
True 334
 
9.1%
2025-08-03T13:08:55.736479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, este
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3539 
True
 
140
ValueCountFrequency (%)
False 3539
96.2%
True 140
 
3.8%
2025-08-03T13:08:55.828922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, este, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3647 
True
 
32
ValueCountFrequency (%)
False 3647
99.1%
True 32
 
0.9%
2025-08-03T13:08:55.925510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

orientation_sur, oeste
Boolean

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.7 KiB
False
3539 
True
 
140
ValueCountFrequency (%)
False 3539
96.2%
True 140
 
3.8%
2025-08-03T13:08:56.002143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Interactions

2025-08-03T13:08:40.124105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:31.877516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.912182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:34.552043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.583361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.715172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.844112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.967803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:40.318745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.020791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.053588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:34.675990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.749564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.863349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.970764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.108366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:40.522552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.146433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.172675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:34.806774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.892135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.992077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.105254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.234386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:40.682758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.263559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.286420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:34.918816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.018035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.133651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.235247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.367036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:40.860178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.386985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.408676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.034583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.163627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.293355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.396636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.498279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:41.034779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.527754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.553696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.179128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.321392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.434478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.534167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.645122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:41.210673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.661846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.675666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.299406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.457608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.568938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.675676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.834609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:41.396889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:32.798046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:33.807810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:35.458372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:36.589483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:37.704074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:38.825548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-03T13:08:39.954011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-03T13:08:56.233656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
air_conditionerbalconybath_numbuilt_in_wardrobechimneycondition_promoción de obra nuevacondition_segunda mano/buen estadocondition_segunda mano/para reformarconstruct_dateenergetic_certiffloorgaragegardenheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: bomba de frío/calorheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanoheating_no dispone de calefacciónhouse_idhouse_typeliftloc_zonem2_realm2_usefulorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_norte, sur, este, oesteorientation_norte, sur, oesteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oestepricereduced_mobilityroom_numstorage_roomswimming_poolterrace
air_conditioner1.0000.0450.2060.1040.0240.0000.0290.0000.0000.0460.0530.0640.1080.0000.0000.0680.0550.0270.0000.0000.0000.0000.0000.1110.0000.1620.0000.1510.0000.0280.0000.0000.0000.0000.0000.0000.0460.0000.0000.0190.0000.0120.0000.2140.0000.1420.0000.1030.069
balcony0.0451.0000.0660.1020.0370.0000.0570.0000.0600.0000.1470.0660.1180.0000.0450.0190.0920.0000.0790.1220.0620.0000.1470.0720.0000.1400.0000.0400.0160.0380.0000.0000.0000.0000.0140.0000.0860.0130.0000.0150.0020.0000.0000.0520.1220.0430.0830.0280.022
bath_num0.2060.0661.0000.0910.0650.0020.0330.0000.2420.0600.5320.1470.2200.0180.0000.0000.0790.0000.0000.0000.0280.000-0.0480.0460.0770.0620.6550.5570.0310.0000.1090.0000.0000.0000.0410.0000.1770.0000.0000.0000.0000.0230.0000.6840.0480.5940.0000.1870.079
built_in_wardrobe0.1040.1020.0911.0000.0000.0700.0820.1150.0280.0450.2240.1870.1970.0700.0390.0000.0740.0000.0000.0570.0430.0000.0670.1620.0860.0670.0190.0730.0000.0810.0000.0140.0000.0000.0000.0220.1000.0710.0000.0500.0610.0300.0610.2180.1030.1100.1110.1010.150
chimney0.0240.0370.0650.0001.0000.0000.0000.0470.2060.1130.2740.0000.0750.0100.0000.0000.0170.0000.0000.0360.0070.0300.0520.5010.0590.2000.1040.1820.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0000.0000.0290.0250.2300.0000.0000.000
condition_promoción de obra nueva0.0000.0000.0020.0700.0001.0000.3250.0470.0380.2270.1780.0370.0300.0120.0000.0000.0340.0000.0000.0560.0000.0000.1160.0660.0610.1020.0000.0150.0230.0420.0040.0000.0000.0000.0360.0000.0170.0000.0180.0420.0200.0000.0200.0900.0420.0000.1450.0230.118
condition_segunda mano/buen estado0.0290.0570.0330.0820.0000.3251.0000.7330.1130.2180.1600.2260.1140.0000.0510.0000.0410.0000.0000.1300.0350.0850.0540.2280.0000.0850.0630.0660.0300.0540.0000.0330.0000.0250.0160.0220.0510.0120.0070.0310.0000.0180.0350.0970.1020.1050.1990.0550.115
condition_segunda mano/para reformar0.0000.0000.0000.1150.0470.0470.7331.0000.1530.0810.1110.1750.0770.0320.0280.0000.0100.0000.0000.0690.0200.1230.0160.2660.0600.1200.0920.1040.0000.0000.0000.0000.0000.0140.0200.0000.0210.0000.0560.0000.0110.0000.0110.1210.0510.1650.0900.0340.089
construct_date0.0000.0600.2420.0280.2060.0380.1130.1531.0000.0890.1120.1730.1230.1150.0580.1950.0360.0000.1140.0470.0000.138-0.0320.0200.1220.0760.1340.1590.0000.1290.0490.0000.0000.0000.0950.0000.0510.0000.0000.0300.0300.0000.0340.1330.0730.0520.1180.0000.107
energetic_certif0.0460.0000.0600.0450.1130.2270.2180.0810.0891.0000.1550.0320.0620.0490.0530.0620.0450.0000.0000.1660.0520.0310.2150.1470.0000.1760.0000.0840.0170.0610.0070.0540.0460.0620.0520.0000.0790.0000.0410.0000.0000.0000.0000.0170.0370.0730.1900.0000.085
floor0.0530.1470.5320.2240.2740.1780.1600.1110.1120.1551.0000.3460.5270.1560.1610.0780.2190.0000.2130.2290.0000.0000.1200.3110.3540.2560.0000.3430.1280.1590.2400.2010.0670.1840.1610.0000.2990.0000.1440.1320.0370.0680.0730.1330.2230.2340.2070.1620.167
garage0.0640.0660.1470.1870.0000.0370.2260.1750.1730.0320.3461.0000.3470.0000.0190.0000.0790.0000.0450.0540.0400.0090.0390.3070.2600.1560.0300.0920.0260.0000.0690.0670.0000.0490.0000.0000.1380.0470.0370.0000.0200.0390.0420.3690.1320.1880.2280.0810.213
garden0.1080.1180.2200.1970.0750.0300.1140.0770.1230.0620.5270.3471.0000.0670.0280.0360.0730.0000.0000.0000.0650.0000.1490.4950.1090.3020.0520.2180.0580.0000.0510.0280.0000.0000.0410.0360.2120.0000.0580.0490.0250.0590.0600.3400.0160.2760.0350.3300.145
heating_calefacción central0.0000.0000.0180.0700.0100.0120.0000.0320.1150.0490.1560.0000.0671.0000.0070.0000.0390.0000.0000.0630.0000.0000.0740.0860.0600.1020.0000.0240.0550.0490.0000.0000.0000.0000.0000.0000.0210.0000.0000.0070.0000.0000.0000.0320.0380.0000.0870.0180.022
heating_calefacción central: gas0.0000.0450.0000.0390.0000.0000.0510.0280.0580.0530.1610.0190.0280.0071.0000.0000.0300.0000.0000.0510.0000.0000.0640.0800.0500.0810.0000.0000.0490.0440.0000.0100.0000.0000.0000.0000.0130.0410.0000.0000.0000.0000.0000.0170.1090.0000.0270.0200.027
heating_calefacción central: gasoil0.0680.0190.0000.0000.0000.0000.0000.0000.1950.0620.0780.0000.0360.0000.0001.0000.0180.0000.0000.0370.0000.0000.0810.0000.0330.0340.0000.0000.0000.0130.0000.0000.0260.0000.0370.0300.0000.0000.0210.0000.0000.0000.0000.0000.1000.0000.0380.0000.018
heating_calefacción individual0.0550.0920.0790.0740.0170.0340.0410.0100.0360.0450.2190.0790.0730.0390.0300.0181.0000.0000.0000.1040.0080.0000.0800.1290.0620.0640.0000.0470.0000.0100.0000.0000.0000.0150.0420.0000.0970.0000.0000.0210.0000.0000.0000.0870.0000.0620.0240.0170.036
heating_calefacción individual: bomba de frío/calor0.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
heating_calefacción individual: eléctrica0.0000.0790.0000.0000.0000.0000.0000.0000.1140.0000.2130.0450.0000.0000.0000.0000.0000.0001.0000.0180.0000.0000.0000.0000.0410.0600.0000.0000.0000.0300.0000.0000.0000.0000.0000.0000.0050.0000.0000.0140.0000.0000.0000.0320.0000.0000.0000.0000.017
heating_calefacción individual: gas natural0.0000.1220.0000.0570.0360.0560.1300.0690.0470.1660.2290.0540.0000.0630.0510.0370.1040.0000.0181.0000.0270.0100.2460.1230.0200.1190.0000.0650.0280.1470.0000.0510.0000.0200.1160.0030.0120.0390.0000.0000.0300.0310.0000.0570.2260.0930.0760.0340.000
heating_calefacción individual: gas propano/butano0.0000.0620.0280.0430.0070.0000.0350.0200.0000.0520.0000.0400.0650.0000.0000.0000.0080.0000.0000.0271.0000.0000.0000.1090.0000.1010.0000.0690.0000.0000.0000.0000.0000.0000.0000.0420.0740.0000.0000.0000.0000.0000.0000.0690.0370.0450.0000.0740.000
heating_no dispone de calefacción0.0000.0000.0000.0000.0300.0000.0850.1230.1380.0310.0000.0090.0000.0000.0000.0000.0000.0000.0000.0100.0001.0000.0000.0620.0460.0730.1230.0000.0000.0020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0040.0000.0120.0000.0000.0000.0050.000
house_id0.0000.147-0.0480.0670.0520.1160.0540.016-0.0320.2150.1200.0390.1490.0740.0640.0810.0800.0000.0000.2460.0000.0001.0000.0010.1140.133-0.083-0.0770.0330.0880.0000.0300.0560.0000.0670.0180.0230.0000.0200.0610.0470.0000.0780.0250.104-0.0480.0550.0680.031
house_type0.1110.0720.0460.1620.5010.0660.2280.2660.0200.1470.3110.3070.4950.0860.0800.0000.1290.0000.0000.1230.1090.0620.0011.0000.2700.264-0.1080.0210.0630.0780.0670.0630.0340.0270.0940.0460.3590.0920.0680.0000.0480.0230.137-0.0580.108-0.0880.1570.1920.117
lift0.0000.0000.0770.0860.0590.0610.0000.0600.1220.0000.3540.2600.1090.0600.0500.0330.0620.0000.0410.0200.0000.0460.1140.2701.0000.2030.0000.0760.0000.0500.0000.0110.0000.0080.0340.0230.0720.0000.0140.0000.0000.0000.0140.3610.1200.1590.1410.0510.119
loc_zone0.1620.1400.0620.0670.2000.1020.0850.1200.0760.1760.2560.1560.3020.1020.0810.0340.0640.0250.0600.1190.1010.0730.1330.2640.2031.0000.0560.0870.0660.1230.0420.0690.0000.0480.1110.0640.1350.0120.0640.0630.0320.0300.0570.1240.1190.1040.2290.2790.139
m2_real0.0000.0000.6550.0190.1040.0000.0630.0920.1340.0000.0000.0300.0520.0000.0000.0000.0000.0000.0000.0000.0000.123-0.083-0.1080.0000.0561.0000.6480.0000.0000.0000.0000.0000.0000.0000.0000.0510.0000.0000.0350.0000.0000.0000.6040.0000.6830.0320.0400.000
m2_useful0.1510.0400.5570.0730.1820.0150.0660.1040.1590.0840.3430.0920.2180.0240.0000.0000.0470.0000.0000.0650.0690.000-0.0770.0210.0760.0870.6481.0000.0540.0000.1100.0000.0000.0000.0440.0000.1590.0000.0000.0310.0000.0730.0510.4990.0100.5390.0000.1610.053
orientation_este0.0000.0160.0310.0000.0000.0230.0300.0000.0000.0170.1280.0260.0580.0550.0490.0000.0000.0000.0000.0280.0000.0000.0330.0630.0000.0660.0000.0541.0000.0630.0220.0160.0000.0000.0550.0000.0320.0000.0330.0630.0350.0000.0350.0280.0000.0640.0450.0160.000
orientation_este, oeste0.0280.0380.0000.0810.0000.0420.0540.0000.1290.0610.1590.0000.0000.0490.0440.0130.0100.0000.0300.1470.0000.0020.0880.0780.0500.1230.0000.0000.0631.0000.0420.0350.0180.0220.0870.0140.0540.0150.0550.0970.0590.0180.0590.0330.1010.0380.0770.0130.049
orientation_norte0.0000.0000.1090.0000.0000.0040.0000.0000.0490.0070.2400.0690.0510.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0670.0000.0420.0000.1100.0220.0421.0000.0000.0000.0000.0360.0000.0160.0000.0170.0410.0190.0000.0190.1060.0000.1200.0220.0000.011
orientation_norte, este0.0000.0000.0000.0140.0000.0000.0330.0000.0000.0540.2010.0670.0280.0000.0100.0000.0000.0000.0000.0510.0000.0000.0300.0630.0110.0690.0000.0000.0160.0350.0001.0000.0000.0000.0290.0000.0090.0000.0100.0340.0130.0000.0130.0060.0550.0480.0480.0170.000
orientation_norte, este, oeste0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0670.0000.0000.0000.0000.0260.0000.0000.0000.0000.0000.0000.0560.0340.0000.0000.0000.0000.0000.0180.0000.0001.0000.0000.0120.0000.0000.0000.0000.0170.0000.0000.0000.0000.0230.0000.0000.0000.000
orientation_norte, oeste0.0000.0000.0000.0000.0000.0000.0250.0140.0000.0620.1840.0490.0000.0000.0000.0000.0150.0000.0000.0200.0000.0000.0000.0270.0080.0480.0000.0000.0000.0220.0000.0000.0001.0000.0170.0000.0000.0000.0000.0220.0000.0000.0000.0530.0550.0490.0640.0000.041
orientation_norte, sur0.0000.0140.0410.0000.0000.0360.0160.0200.0950.0520.1610.0000.0410.0000.0000.0370.0420.0000.0000.1160.0000.0000.0670.0940.0340.1110.0000.0440.0550.0870.0360.0290.0120.0171.0000.0080.0470.0090.0480.0860.0510.0130.0510.0490.0740.0460.0680.0290.042
orientation_norte, sur, este0.0000.0000.0000.0220.0000.0000.0220.0000.0000.0000.0000.0000.0360.0000.0000.0300.0000.0000.0000.0030.0420.0000.0180.0460.0230.0640.0000.0000.0000.0140.0000.0000.0000.0000.0081.0000.0000.0000.0000.0140.0000.0000.0000.0000.0000.0000.0110.0000.000
orientation_norte, sur, este, oeste0.0460.0860.1770.1000.0000.0170.0510.0210.0510.0790.2990.1380.2120.0210.0130.0000.0970.0000.0050.0120.0740.0000.0230.3590.0720.1350.0510.1590.0320.0540.0160.0090.0000.0000.0470.0001.0000.0000.0260.0540.0290.0000.0290.2310.0000.2110.0250.1240.060
orientation_norte, sur, oeste0.0000.0130.0000.0710.0000.0000.0120.0000.0000.0000.0000.0470.0000.0000.0410.0000.0000.0000.0000.0390.0000.0000.0000.0920.0000.0120.0000.0000.0000.0150.0000.0000.0000.0000.0090.0000.0001.0000.0000.0150.0000.0000.0000.1420.0140.0000.0450.0000.030
orientation_oeste0.0000.0000.0000.0000.0000.0180.0070.0560.0000.0410.1440.0370.0580.0000.0000.0210.0000.0000.0000.0000.0000.0000.0200.0680.0140.0640.0000.0000.0330.0550.0170.0100.0000.0000.0480.0000.0260.0001.0000.0550.0300.0000.0300.0480.0550.0660.0450.0000.000
orientation_sur0.0190.0150.0000.0500.0450.0420.0310.0000.0300.0000.1320.0000.0490.0070.0000.0000.0210.0000.0140.0000.0000.0440.0610.0000.0000.0630.0350.0310.0630.0970.0410.0340.0170.0220.0860.0140.0540.0150.0551.0000.0580.0180.0580.0200.0000.0000.0280.0310.000
orientation_sur, este0.0000.0020.0000.0610.0000.0200.0000.0110.0300.0000.0370.0200.0250.0000.0000.0000.0000.0000.0000.0300.0000.0000.0470.0480.0000.0320.0000.0000.0350.0590.0190.0130.0000.0000.0510.0000.0290.0000.0300.0581.0000.0000.0320.0040.0300.0040.0460.0090.000
orientation_sur, este, oeste0.0120.0000.0230.0300.0000.0000.0180.0000.0000.0000.0680.0390.0590.0000.0000.0000.0000.0000.0000.0310.0000.0040.0000.0230.0000.0300.0000.0730.0000.0180.0000.0000.0000.0000.0130.0000.0000.0000.0000.0180.0001.0000.0000.1060.0210.0330.0000.0240.027
orientation_sur, oeste0.0000.0000.0000.0610.0000.0200.0350.0110.0340.0000.0730.0420.0600.0000.0000.0000.0000.0000.0000.0000.0000.0000.0780.1370.0140.0570.0000.0510.0350.0590.0190.0130.0000.0000.0510.0000.0290.0000.0300.0580.0320.0001.0000.0660.0000.0610.0190.0000.038
price0.2140.0520.6840.2180.0290.0900.0970.1210.1330.0170.1330.3690.3400.0320.0170.0000.0870.0000.0320.0570.0690.0120.025-0.0580.3610.1240.6040.4990.0280.0330.1060.0060.0000.0530.0490.0000.2310.1420.0480.0200.0040.1060.0661.0000.0340.4940.0650.1780.197
reduced_mobility0.0000.1220.0480.1030.0250.0420.1020.0510.0730.0370.2230.1320.0160.0380.1090.1000.0000.0000.0000.2260.0370.0000.1040.1080.1200.1190.0000.0100.0000.1010.0000.0550.0230.0550.0740.0000.0000.0140.0550.0000.0300.0210.0000.0341.0000.0290.1330.0000.051
room_num0.1420.0430.5940.1100.2300.0000.1050.1650.0520.0730.2340.1880.2760.0000.0000.0000.0620.0000.0000.0930.0450.000-0.048-0.0880.1590.1040.6830.5390.0640.0380.1200.0480.0000.0490.0460.0000.2110.0000.0660.0000.0040.0330.0610.4940.0291.0000.0450.1420.097
storage_room0.0000.0830.0000.1110.0000.1450.1990.0900.1180.1900.2070.2280.0350.0870.0270.0380.0240.0000.0000.0760.0000.0000.0550.1570.1410.2290.0320.0000.0450.0770.0220.0480.0000.0640.0680.0110.0250.0450.0450.0280.0460.0000.0190.0650.1330.0451.0000.0670.283
swimming_pool0.1030.0280.1870.1010.0000.0230.0550.0340.0000.0000.1620.0810.3300.0180.0200.0000.0170.0000.0000.0340.0740.0050.0680.1920.0510.2790.0400.1610.0160.0130.0000.0170.0000.0000.0290.0000.1240.0000.0000.0310.0090.0240.0000.1780.0000.1420.0671.0000.082
terrace0.0690.0220.0790.1500.0000.1180.1150.0890.1070.0850.1670.2130.1450.0220.0270.0180.0360.0000.0170.0000.0000.0000.0310.1170.1190.1390.0000.0530.0000.0490.0110.0000.0000.0410.0420.0000.0600.0300.0000.0000.0000.0270.0380.1970.0510.0970.2830.0821.000

Missing values

2025-08-03T13:08:41.863015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-03T13:08:42.679557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-03T13:08:43.313195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ad_last_updateair_conditionerbalconybath_numbuilt_in_wardrobechimneyconstruct_dateenergetic_certiffloorgaragegardenhouse_idhouse_typeliftloc_cityloc_districtloc_fullloc_neighloc_zonem2_realm2_usefulobtention_datepricereduced_mobilityroom_numstorage_roomswimming_poolterracecondition_promoción de obra nuevacondition_segunda mano/buen estadocondition_segunda mano/para reformarheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: bomba de frío/calorheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanoheating_no dispone de calefacciónorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_norte, sur, este, oesteorientation_norte, sur, oesteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oeste
0Anuncio actualizado el 27 de marzo002.00001993.00NaN2 plantas118171763401.00UrcabustaizLa iglesiaLa iglesia , Urcabustaiz , Zuya, ÁlavaNaNZuya, Álava1000172.002019-03-2931000004.00001FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
1más de 5 meses sin actualizar002.00002006.00no indicadoplanta 2ª exterior002958807411.00Legutianola Union Plazatxoa, 8la Union Plazatxoa, 8 , Legutiano , Zuya, ÁlavaNaNZuya, Álava8687.002019-03-2913900013.00100FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2más de 5 meses sin actualizar003.00001993.00no indicado3 plantas113745311621.00AramaioCalle Etxaguen s/nCalle Etxaguen s/n , Aramaio , Zuya, ÁlavaNaNZuya, Álava300087.002019-03-2948000004.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3más de 5 meses sin actualizar011.00111993.00en trámite3 plantas018256891821.00UrcabustaizCP 01449, 1449 CPCP 01449, 1449 CP , Urcabustaiz , Zuya, ÁlavaNaNZuya, Álava8687.002019-03-2915000004.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4más de 5 meses sin actualizar001.00001993.00no indicadoplanta 1ª exterior112913524231.00UrcabustaizCalle Nueva Plaza, 9Calle Nueva Plaza, 9 , Urcabustaiz , Zuya, ÁlavaNaNZuya, Álava7674.002019-03-299000002.00111FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrue
5más de 5 meses sin actualizar002.00001993.00no indicado3 plantas103614133501.00ZuyaCarretera Guillerna, 11Carretera Guillerna, 11 , Zuya , ÁlavaNaNÁlava1000273.002019-03-2954500004.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
6más de 5 meses sin actualizar002.00001993.00no indicadoplanta 1ª exterior113844069831.00UrcabustaizCalle Ugarte, 8Calle Ugarte, 8 , Urcabustaiz , Zuya, ÁlavaNaNZuya, Álava10087.002019-03-2919500013.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalse
7más de 5 meses sin actualizar003.00011993.00no indicadoNaN108117858941.00LegutianoCalle olaeta s/nCalle olaeta s/n , Legutiano , Zuya, ÁlavaNaNZuya, Álava80087.002019-03-2935000007.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
8más de 5 meses sin actualizar002.00001993.00no indicadoplanta 2ª exterior0036603611.00LegutianoPlaza Unión, 8Plaza Unión, 8 , Legutiano , Zuya, ÁlavaNaNZuya, Álava8280.002019-03-2913900003.00100FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
9más de 5 meses sin actualizar013.00101993.00NaN2 plantas114021938901.00ZigoitiaBasalanda Kalea, Etxabarri Ibiña, 19Basalanda Kalea, Etxabarri Ibiña, 19 , Zigoitia , Zuya, ÁlavaNaNZuya, Álava240210.002019-03-2949900004.00001FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
ad_last_updateair_conditionerbalconybath_numbuilt_in_wardrobechimneyconstruct_dateenergetic_certiffloorgaragegardenhouse_idhouse_typeliftloc_cityloc_districtloc_fullloc_neighloc_zonem2_realm2_usefulobtention_datepricereduced_mobilityroom_numstorage_roomswimming_poolterracecondition_promoción de obra nuevacondition_segunda mano/buen estadocondition_segunda mano/para reformarheating_calefacción centralheating_calefacción central: gasheating_calefacción central: gasoilheating_calefacción individualheating_calefacción individual: bomba de frío/calorheating_calefacción individual: eléctricaheating_calefacción individual: gas naturalheating_calefacción individual: gas propano/butanoheating_no dispone de calefacciónorientation_esteorientation_este, oesteorientation_norteorientation_norte, esteorientation_norte, este, oesteorientation_norte, oesteorientation_norte, surorientation_norte, sur, esteorientation_norte, sur, este, oesteorientation_norte, sur, oesteorientation_oesteorientation_surorientation_sur, esteorientation_sur, este, oesteorientation_sur, oeste
3669Anuncio actualizado el 21 de enero002.00101981.00NaNplanta 1ª exterior108134203131.00Vitoria-GasteizDistrito San CristóbalCalle Flandes , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle FlandesÁlava120110.002019-03-2927950003.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
3670Anuncio actualizado el 20 de marzo001.00001993.00NaNexterior008474057331.00Vitoria-GasteizDistrito San CristóbalCalle Herminio Madinaveitia , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle Herminio MadinaveitiaÁlava6943.002019-03-298300002.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3671Anuncio actualizado el 16 de marzo002.00001993.00NaNexterior008286884631.00Vitoria-GasteizDistrito San CristóbalCalle Iturritxu , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle IturritxuÁlava11384.002019-03-2924000003.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3672Anuncio actualizado el 6 de marzo001.00001993.00NaNexterior003488367230.00Vitoria-GasteizDistrito San CristóbalCalle Campo de los Palacios , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle Campo de los PalaciosÁlava6153.002019-03-299600002.00000FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalse
3673Anuncio actualizado el 6 de marzo001.00001993.00NaNexterior003488367431.00Vitoria-GasteizDistrito San CristóbalCalle Ferrocarril , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle FerrocarrilÁlava7968.002019-03-2920000002.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3674Anuncio actualizado el 6 de marzo001.00001993.00NaNexterior008407174831.00Vitoria-GasteizDistrito San CristóbalZumakadi Ibilbidea , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaZumakadi IbilbideaÁlava7054.002019-03-2913500002.00100FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
3675Anuncio actualizado el 1 de marzo002.00101981.00NaNplanta 1ª exterior108427346531.00Vitoria-GasteizDistrito San CristóbalCalle Flandes , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle FlandesÁlava120110.002019-03-2927950003.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
3676Anuncio actualizado el 28 de marzo001.00001984.00en trámiteplanta 6ª exterior008483822831.00Vitoria-GasteizDistrito San CristóbalPlaza de la zumaquera , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaPlaza de la zumaqueraÁlava8272.002019-03-2914500002.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
3677Anuncio actualizado el 25 de marzo002.00101990.00NaNplanta 6ª exterior008480520311.00Vitoria-GasteizDistrito San CristóbalComandante izarduy , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaComandante izarduyÁlava6960.002019-03-2926800002.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse
3678Anuncio actualizado el 23 de marzo001.00101990.00NaNplanta 6ª004056715411.00Vitoria-GasteizDistrito San CristóbalCalle Comandante Izarduy , Distrito San Cristóbal , Vitoria-Gasteiz , ÁlavaCalle Comandante IzarduyÁlava7060.002019-03-2926800002.00101FalseTrueFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalseFalseFalse